WO2016039765A1 - Suppression d'interférence résiduelle - Google Patents

Suppression d'interférence résiduelle Download PDF

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Publication number
WO2016039765A1
WO2016039765A1 PCT/US2014/055329 US2014055329W WO2016039765A1 WO 2016039765 A1 WO2016039765 A1 WO 2016039765A1 US 2014055329 W US2014055329 W US 2014055329W WO 2016039765 A1 WO2016039765 A1 WO 2016039765A1
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Prior art keywords
psd
residual interference
output
late
reference signal
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PCT/US2014/055329
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English (en)
Inventor
Markus Buck
Tobias Wolff
Naveen Kumar DESIRAJU
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Nuance Communications, Inc.
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Priority to PCT/US2014/055329 priority Critical patent/WO2016039765A1/fr
Priority to US15/508,140 priority patent/US10056092B2/en
Publication of WO2016039765A1 publication Critical patent/WO2016039765A1/fr

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L21/0232—Processing in the frequency domain
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/21—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being power information
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L2021/02082—Noise filtering the noise being echo, reverberation of the speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones

Definitions

  • adaptive interference cancellation can form a part of acoustic echo cancellation, adaptive beam forming, adaptive noise cancellation, etc.
  • AIC uses adaptive filters to model the acoustic (reverberant) channel of the interfering signal component. The estimate of the interference component is then subtracted ("cancelled") from the input signal without distorting the desired signal component. Nevertheless, some residual interference remains after AIC .
  • residual interference suppression RIS is applied after AIC, which performs spectral weighting on the AIC output.
  • Embodiments of the invention provide methods and apparatus for providing an enhanced estimation of the power of the residual interference component after AIC by achieving a higher accuracy for less speech distortion than conventional techniques.
  • inventive RIS processing a better signal quality (i.e. a better trade-off between distortion of the desired signal and suppression of the interference) is achieved.
  • AEC barge-in applications
  • beamformer post filtering is achieved.
  • the residual component of the interference includes multiple parts.
  • One part is due to the limited length of the adaptive filter: the full length of the acoustic path cannot be modeled and late echoes cannot be cancelled with AIC.
  • Another part is due to a misalignment of the adaptive filter: as the acoustic path changes over time the filter has to adapt permanently and is never perfectly converged.
  • Embodiments of the invention improve the accuracy of the power estimate based on an inventive parametric model. With the inventive processing, improved speech enhancement (less speech distortion) and improved ASR performance are achieved.
  • Embodiments of the invention can also provide adaptation control of the filters within the AIC to allow for a more precise estimate of the misalignment of the filter which is required to calculate the optimal step size for filter adaptation. This improves the AIC performance.
  • embodiments of the invention are applicable to a wide range of applications, such as ASR and hands-free telephony applications, barge-in, acoustic echo cancellation, multichannel reverberation suppression, and the like.
  • a method for estimating reverberant spectral variance comprises: estimating a power spectral density of residual interference after adaptive interference cancellation (AIC) using first and second components; estimating the first component using a real-valued FIR filter operating on a power spectral density (PSD) of a reference signal; and estimating the second component using an exponential decay over time corresponding to a reverberation time using the PSD of the reference signal.
  • AIC adaptive interference cancellation
  • PSD power spectral density
  • the method can further include one or more of the following features: using a FIR filter for the first component and using an IIR filter for the second component, the FIR filter has a number of taps, the IIR filter includes a delay element with the delay equal to the length of the FIR filter, determining a common scaling factor for the taps, using gradient descend processing to find the first and/or second component, using equation error principle processing, using a logarithmic cost function for the gradient descend processing, determining parameters A, B, and C and compensating the first component, which corresponds to an early reverberation PSD, from an observed PSD, to drive the adaptation of the parameter B, where the parameter A is a scaling parameter corresponding to a strength of late reverberation, the parameter B describes exponential decay in relation to reverberation time of an enclosure and the parameter C is a common scaling factor for filter time lags, determining the parameter B by extrapolating a log of an AEC filter response linearly and using the resulting late reverb-PSD jointly with an FIR Model
  • an article comprises: a non-transitory storage medium having stored instructions that enable a machine to estimate reverberant spectral variance (RSV), comprising instructions to: estimate a power spectral density of residual interference after adaptive interference cancellation (AIC) using first and second components; estimate the first component using a real-valued FIR filter operating on a power spectral density (PSD) of a reference signal; and estimate the second component using an exponential decay over time corresponding to a reverberation time using the PSD of the reference signal.
  • RSV reverberant spectral variance
  • the article can further include one or more of the following features: using a FIR filter for the first component and using an IIR filter for the second component, the FIR filter has a number of taps, the IIR filter includes a delay element with the delay equal to the length of the FIR filter, determining a common scaling factor for the taps, using gradient descend processing to find the first and/or second component, using equation error principle processing, using a logarithmic cost function for the gradient descend processing, determining parameters A, B, and C and compensating the first component, which corresponds to an early reverberation PSD, from an observed PSD, to drive the adaptation of the parameter B, where the parameter A is a scaling parameter corresponding to a strength of late reverberation, the parameter B describes exponential decay in relation to reverberation time of an enclosure and the parameter C is a common scaling factor for filter time lags, determining the parameter B by extrapolating a log of an AEC filter response linearly and using the resulting late reverb-PSD jointly with an FIR Model
  • a system comprises: an AIC module to receive an input signal and a reference signal and generate an AIC output signal; a first PSD module to receive the AIC output signal and generate a first PSD output signal; a second PSD module to receive the reference signal and generate a second PSD output signal; an early and late residual interference PSD estimation module to receive the second PSD output signal and generate an early residual interference output and a late residual interference output, the early and late residual interference PSD estimation module configured to generate the early residual interference output using a real-valued FIR filter operating on a power spectral density (PSD) of the reference signal, and to generate the late residual interference output using an exponential decay over time corresponding to a reverberation time using the PSD of the reference signal; and a residual echo suppression module to process the early residual interference output, the late residual interference output, and the AIC output.
  • PSD power spectral density
  • the system can be further configured to include one or more of the following features: using a FIR filter for the first component and using an IIR filter for the second component, the FIR filter has a number of taps, the IIR filter includes a delay element with the delay equal to the length of the FIR filter, determining a common scaling factor for the taps, using gradient descend processing to find the first and/or second component, using equation error principle processing, using a logarithmic cost function for the gradient descend processing, determining parameters A, B, and C and compensating the first component, which corresponds to an early reverberation PSD, from an observed PSD, to drive the adaptation of the parameter B, where the parameter A is a scaling parameter corresponding to a strength of late reverberation, the parameter B describes exponential decay in relation to reverberation time of an enclosure and the parameter C is a common scaling factor for filter time lags, determining the parameter B by extrapolating a log of an AEC filter response linearly and using the resulting late reverb-PSD jointly with an
  • a system comprises: an AIC module to receive an input signal and a reference signal and generate an AIC output signal; a first PSD module to receive the AIC output signal and generate a first PSD output signal; a second PSD module to receive the reference signal and generate a second PSD output signal; an early and late residual interference PSD estimation module to receive the second PSD output signal and generate an early residual interference output and a late residual interference output, the early and late residual interference PSD estimation module configured to generate the early residual interference output using a real-valued FIR filter operating on a power spectral density (PSD) of the reference signal, and to generate the late residual interference output using an exponential decay over time corresponding to a reverberation time using the PSD of the reference signal; a beamforming module to receive the input signal and the reference signal and generate a beamforming output signal; and a dereverberation module to process the late residual interference output and the
  • the system can be further configured to include one or more of the following features: using a FIR filter for the first component and using an IIR filter for the second component, the FIR filter has a number of taps, the IIR filter includes a delay element with the delay equal to the length of the FIR filter, determining a common scaling factor for the taps, using gradient descend processing to find the first and/or second component, using equation error principle processing, using a logarithmic cost function for the gradient descend processing, determining parameters A, B, and C and compensating the first component, which corresponds to an early reverberation PSD, from an observed PSD, to drive the adaptation of the parameter B, where the parameter A is a scaling parameter corresponding to a strength of late reverberation, the parameter B describes exponential decay in relation to reverberation time of an enclosure and the parameter C is a common scaling factor for filter time lags, determining the parameter B by extrapolating a log of an AEC filter response linearly and using the resulting late reverb-PSD jointly with an
  • FIG. 1 is a schematic representation of an adaptive interference cancellation (AIC) system
  • FIG. 1 A is a schematic representation of a AIC system for acoustic echo cancellation
  • FIG. I B is a schematic representation of a AIC system for adaptive signal blocking (e.g. in the context of adaptive beamforming);
  • FIG. 2 is a schematic representation of a graphical representation of a convolutive model for residual interference power spectral density (PSD);
  • FIG. 3 is a schematic representation for a residual interference PSD having a early components processed with a FIR filter and late components processed with a IIR filter;
  • FIG. 4 is a schematic representation of a FIR filter for early residual PSD
  • FIG. 4A is a schematic representation of an IIR model for late residual PSD
  • FIG. 5 is a schematic representation of a system to minimize output error
  • FIG. 5A is a schematic representation of a system for adaptation of an IIR filter
  • FIG. 5B is a schematic representation of a further system for adaptation of an IIR filter converted into an FIR filter using the equation error principle
  • FIG. 6 is a schematic representation of an adaptive filter structure
  • FIG. 6A is a schematic representation of a further adaptive filter structure
  • FIG. 7 is a schematic representation of a system for generating an estimate of the residual interference PSD to be used for residual echo suppression
  • FIG. 7A is a schematic representation of a system for generating and using a residual estimate for late reverberation suppression at beamformer output
  • FIG. 7B is a flow diagram for an illustrative sequence of steps for residual interference suppression.
  • FIG. 8 is a schematic representation of an illustrative computer that can perform at least a portion of the processing described herein.
  • FIG. 1 shows a high level system 100 having adaptive interference cancellation (AIC) in accordance with illustrative embodiments of the invention.
  • the AIC system 100 receives a reference signal X(k) for the interference that is filtered by a D-tap adaptive filter 102 H](k), and then subtracted from the AIC input signal Y(k).
  • E(k) is the error signal after subtraction, computed as follows:
  • Hi(k) replicates the transmission characteristics of the interference from its source to the microphone, then subtraction can completely remove the interfering component from the microphone signal Y(k).
  • processing is performed in the short-time Fourier domain with k being the frame index.
  • the frequency index is omitted in the text for better readability.
  • All signals and filter taps are generally complex values. Conjugate complex signals are indicated by an exposed star (*).
  • interfering signal components superpose the desired speech signal.
  • a reference signal of the interfering sound source is available.
  • An interfering source can include, e.g., a loudspeaker, or even parts of the desired signal such as reverberation.
  • AIC adaptive interference cancellation
  • FIG. 1A shows a system 150 having acoustic echo cancellation (AEC), where X(k) is the playback signal of a loudspeaker 152 and Y(k) is the microphone signal that includes the echo of the loudspeaker coupling over the room into the microphone 154.
  • AEC acoustic echo cancellation
  • FIG. IB shows a further AIC system 160 for signal blocking (e.g.in the context of adaptive beamforming).
  • the AIC structure 156 filters out the direct component of the speech signal so that the remaining signal components (typically noise) can be reduced at the output of a fixed beamformer (Generalized Sidelobe Canceller), for example.
  • signal blocking can be achieved by simple subtraction of time-aligned adjacent microphone signals (in this case the AIC filter H(k) performs the temporal alignment).
  • the AIC structure can be applied for more accurate cancellation of the early reverberant components.
  • the term satisfyingBlocking matrix is used in the context of the Generalized Sidelobe Canceller.
  • FIG. IB has some commonality with the system of FIG. 1A.
  • the illustrated system 160 provides direct sound blocking.
  • X(k) is a microphone 158 signal.
  • the length D of the adaptive filters within the AIC is usually chosen to be relatively short. Therefore, the full length of the acoustic impulse response is not represented by the AIC. As a consequence late echoes (late reverberation) cannot be compensated by AIC and remain in the output signal.
  • the residual interference component includes a number of components.
  • One component is due to a not fully converged adaptive filter (time lags from 0 up to D-l).
  • Another component time lags larger or equal than D is present since it cannot be covered by the adaptive filter due to the finite length of the filter. If the AlC-filter is not converged at all, the first part of the residual interference may actually be the complete interference.
  • FIG. 2 shows a plot 200 of logarithmic amplitude versus filter time lag for a convolutive model 3 ⁇ 4 for the residual interference.
  • Dashed line 202 shows the impulse response i that connects the PSD (power spectral density) of the residual interference ⁇ and the PSD of the reference ⁇ by convolution. It can be seen that the PSD of the late part can be well described by an exponential decay, whereas the early part generally requires more parameters.
  • the solid line 204 shows what is represented by the inventive model for the case of an almost converged AIC-Filter: The first coefficients are nearly identical and the late ones decay exponentially.
  • Embodiments of the invention provide estimation of the reverberant spectral variance (RSV) at the AIC output. It is understood that RSV is identical to the term PSD of the residual interference. In particular, embodiments estimate early and late RSV parts jointly. Embodiments can be applied to residual echo suppression for acoustic echo cancellation and dereverberation in the context of beamforming, for example.
  • RSS reverberant spectral variance
  • the residual error after adaptive interference cancellation is modelled in the power domain, as follows:
  • ⁇ rr(k) is the PSD of the residual interference component after AIC.
  • 3 ⁇ 4 is a frame- based scalar weighting factor which refers to the contribution of the PSD of the reference signal xx(k) of the recent frames.
  • the residual interference PSD is separated into two parts: the first part (time lags 0,..., D - 1) refers to the region which is covered by the adaptive AIC filter of length D and the second part (time lags D,...) refers to the time lags which cannot be modelled b the AIC filter (due to its finite length D), as set forth below:
  • the first part ⁇ ee(k) contributes due to misalignment of the adaptive filter and the second part ⁇ LL(k) contributes due to the late reverberation tail (Eq. 2). Modelling the PSD of the residual interference with the first and second parts provides enhanced performance in comparison with conventional processing.
  • FIG. 3 shows an illustrative implementation 300 for processing residual interference PSD.
  • the early components are modelled using an FIR filter 302 and the late parts are represented by an IIR filter 304.
  • the reference signal xx(k) of the recent frames is provided to the FIR filter 302, which outputs ⁇ ee(k), and the IIR filter 304, which outputs ⁇ L[_(k).
  • ⁇ te(k) and ⁇ L L(k) are combined to generate ⁇ rr(k).
  • FIG. 4 shows a representation of a first part as a D-tap real-valued FIR filter 400 which operates on the PSD of the reference signal, as follows:
  • FIG. 4 shows an illustrative FIR filter 400 for estimating the early residual interference PSD in which the temporal context in the inventive FIR implementation matches the one of the early residual.
  • the FIR model can be simplified by the assumption that Ki is expected to show equal values for all time lags (at least when Hj(k) has converged sufficiently). It is assumed that the misalignment of the adaptive filter coefficients is equally distributed over all taps. Thus, we can apply a common scaling factor C for all time lags as follows:
  • Ki A B 1 , for 1 > D, where B is between 0 and 1 and is closely related to the reverberation time T60 of the enclosing room.
  • A is a scaling parameter that represents the strength of the late reverberation.
  • FIG. 4A shows a first order IIR implementation for late residual interference PSD ⁇ ! (k).
  • three parameters A, B and C are used for estimating the residual interference PSD Orr(k) on the basis of the accessible PSD reference signal ⁇ Pxx(k).
  • a method for estimating A, B, while neglecting the influence of ⁇ Pee(k), is described above.
  • parameter estimation is provided which considers both, Oee(k) and Ou k).
  • parameters A and B can be extracted from the filter coefficients Hi(k).
  • a and B can be found by fitting a substantially straight line to the log (
  • knowledge about A and B can be used to estimate the parameter C.
  • the AIC output PSD ⁇ Dee(k) (which is accessible) is approximately equal to the residual interference PSD ⁇ rr(k).
  • the third parameter C can be estimated as follows:
  • ⁇ rr(k) ⁇ ee (k) + ⁇ Li k) is the RSV estimate by the model and ⁇ rr(k) is the true RSV.
  • FIG. 5 shows an illustrative adaptive filter structure 500 having a FIR filter 502 and a IIR filter 504 for iteratively estimating interference parameters.
  • the IIR component 504 can be handled using the so called equation error principle which breaks up the recursive loop and adapts a feed-forward system instead, which converges to the correct solution.
  • FIG. 5A shows an illustrative adaptive IIR filter 504 implementation with the regular principle .
  • FIG. 5B shows a further adaptive IIR filter implementation for the equation error principle.
  • the recursive path in FIG. 5B with the parameter B is driven by the desired signal ⁇ , ⁇ rather than ⁇ ix(k).
  • FIR feed-forward system
  • FIG. 6 shows an overall adaptive filter system 600 having a FIR filter 602 and an IIR filter 604 for iteratively estimating interference parameters in accordance with the so-called "equation error principle.”
  • FIG. 6A shows an equation error-based filter system where the estimated PSD of the early residual interference is subtracted from the excitation of the recursion parameter B(k).
  • the adaptation can be computed as:
  • ⁇ denotes the stepsize.
  • the normalization term in the denominator includes the D-th input tap that excites the late reverb model.
  • the excitation of the coefficient B is contained in the normalization. If the FIR-model is simplified to the parameter C (assuming all FIR coefficients have the same value), even simpler schemes like the "sign-algorithm" can be used instead. This only evaluates the sign of the gradient resulting in a fixed increase of C if the estimated PSD is too small and a decrease in the opposite case - a logarithmic error function can also be used to find C .
  • the NLMS update of the parameters A and B reads as follows:
  • the temporal context in the error function (Eq. 9) can be utilized. This can for instance be achieved by exponential forgetting. A sliding window may also be used but consumes more memory.
  • the corresponding gradient descent update rule for the FIR filter can be provided as follows:
  • a further refinement of this adaption is to subtract the estimated PSD of the early residual interference ⁇ ee(k) from Orr(k) before feeding it into B(k), as depicted in FIG. 6A.
  • logarithmic cost functions can be applied as well to find A and B by gradient descend.
  • the residual interference PSD Orr(k) is estimated.
  • the PSD of the error signal Oee(k) can be directly accessed.
  • the weighting filter W(k) for the RIS according to the Wiener filter rule is:
  • W(k) is then applied to E(k) to obtain a further enhanced output signal with suppressed interference:
  • This model helps to estimate Orr(k) more accurately and thus reduce the speech distortion which comes due to estimation errors.
  • FIG. 7 shows an implementation of joint estimation of early and late residual interference PSDs for the case of acoustic echo cancellation.
  • the resultant PSD estimates can be used for enhanced residual echo suppression.
  • a microphone signal Y(k) and a reference signal X(k) are provided to an AIC module 702.
  • the reference signal X(k) is also provided to a PSD module 704 coupled to an early and late residual interference PSD estimation module 706.
  • the AIC output is coupled to a PSD module 708 and a residual echo suppression module 710, which also receives the early and late outputs from the early and late residual interference PSD estimation module 706.
  • Hi(k) normalized least-mean square (NLMS) processing can be applied, as follows:
  • the optimal step size for adaptation can be computed as: ®ee (k) (20)
  • Control of the step size ⁇ (1 ⁇ ) enables good convergence behavior.
  • one aim is to get a better estimate of the residual of Oee(k) and thus, to a better convergence of the AIC filter.
  • the dynamic step size enables the filter to adapt (and converge) quickly when ee(k) is large (i.e., the filter is not well converged) and also ensures that the filter adapts slowly when Oee(k) is small (i.e., it prevents the filter from losing good convergence).
  • a benefit of modelling the late reverb here is to get an estimate for the early residual PSD that is not affected by the late residual PSD.
  • the AlC-step- size will be small even if there is significant late reverberant energy. This improves the convergence of the AlC-filter compared to conventional AIC control methods.
  • the length of the adaptive filter should be chosen according to the T60 (A large T60 requires a longer adaptive filter).
  • Mobile devices for example, are used in different acoustic environments with very different T60s. Thus, it may be desirable to adjust D dynamically.
  • the length D of the adaptive filter can be adjusted automatically using a variety of criteria. From parameter B the T60 can be calculated. The length D can be set to a certain (predefined) percentage of T60 (e. g. 60%). Another criterion could be that the ratio of the two error portions equal a certain (predefined) value Q when the filter has converged sufficiently, e.g.,: Oee(k)/ Ou/k) ⁇ Q. It is understood that instead of/ alternatively to this ratio a formula can use purely model parameters.
  • FIG. 7A which has some commonality with FIG. 7, shows use of the improved estimate for late reverberation suppression at the output of a beamfomier.
  • a beamforming module 712 receives microphone signals X(k) and Y(k) and provides an output to a
  • the described estimator for the late reverb 706 can be applied to perform dereverberation 714 on the output signal of the beamformer 712. Thereby, the early reverberation components will not be suppressed as they had been identified by the joint model and are explicitly not fed into the dereverberation filter 714 as illustrated.
  • the blocking matrix gives the AIC filter output for estimating the parameters of a
  • Conventional spatial postfilter techniques can use the blocked PSD ⁇ ee(k) directly for suppressing the reverb, but may suffer from the early reverb components leading to degradations of the desired signal.
  • FIG. 7A shows an illustrative sequence of steps for residual interference suppression processing.
  • an AIC output signal having residual interference is received.
  • the residual interference is estimated including estimating a power spectral density of a first part of the residual interference corresponding to early reverberation and a second part corresponding to late reverberation.
  • the first part is estimated using a real-valued FIR filter operating on a power spectral density (PSD) of a reference signal and the second part is estimated using an exponential decay over time corresponding to a reverberation time using the PSD of the reference signal.
  • PSD power spectral density
  • filter parameters can be adjusted.
  • a filter step size can be optimized for filter convergence.
  • a filter length can be adjusted based upon the reverberation time.
  • FIG. 8 shows an exemplary computer 800 that can perform at least part of the processing described herein.
  • the computer 800 includes a processor 802, a volatile memory 804, a non-volatile memory 806 (e.g., hard disk), an output device 807 and a graphical user interface (GUI) 808 (e.g., a mouse, a keyboard, a display, for example).
  • the non- volatile memory 806 stores computer instructions 812, an operating system 816 and data 818.
  • the computer instructions 812 are executed by the processor 802 out of volatile memory 804.
  • an article 820 comprises non-transitory computer-readable instructions.
  • Processing may be implemented in hardware, software, or a combination of the two.
  • Processing may be implemented in computer programs executed on programmable computers/machines that each includes a processor, a storage medium or other article of manufacture that is readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and one or more output devices.
  • Program code may be applied to data entered using an input device to perform processing and to generate output information.
  • the system can perform processing, at least in part, via a computer program product, (e.g., in a machine-readable storage device), for execution by, or to control the operation of, data processing apparatus (e.g., a programmable processor, a computer, or multiple computers).
  • a computer program product e.g., in a machine-readable storage device
  • data processing apparatus e.g., a programmable processor, a computer, or multiple computers.
  • Each such program may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system.
  • the programs may be implemented in assembly or machine language.
  • the language may be a compiled or an interpreted language and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • a computer program may be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.
  • a computer program may be stored on a storage medium or device (e.g., CD-ROM, hard disk, or magnetic diskette) that is readable by a general or special purpose programmable computer for configuring and operating the computer when the storage medium or device is read by the computer.
  • Processing may also be implemented as a machine-readable storage medium, configured with a computer program, where upon execution, instructions in the computer program cause the computer to operate. Processing may be performed by one or more programmable processors executing one or more computer programs to perform the functions of the system. All or part of the system may be implemented as, special purpose logic circuitry (e.g., an FPGA (field
  • ASIC application-specific integrated circuit

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  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Computational Linguistics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Quality & Reliability (AREA)
  • Circuit For Audible Band Transducer (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
  • Filters That Use Time-Delay Elements (AREA)

Abstract

La présente invention concerne des procédés et un appareil pour estimer la densité spectrale de puissance (PSD) d'une interférence résiduelle ayant des premier et second composants après suppression d'interférence adaptative (AIC). Le premier composant peut être estimé au moyen d'un filtre FIR à valeur réelle agissant sur une série temporelle d'estimations de PSD d'un signal de référence, et le deuxième composant peut être estimé au moyen d'une décroissance exponentielle dans le temps correspondant à un temps de réverbération en utilisant la PSD du signal de référence.
PCT/US2014/055329 2014-09-12 2014-09-12 Suppression d'interférence résiduelle WO2016039765A1 (fr)

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PCT/US2014/055329 WO2016039765A1 (fr) 2014-09-12 2014-09-12 Suppression d'interférence résiduelle
US15/508,140 US10056092B2 (en) 2014-09-12 2014-09-12 Residual interference suppression

Applications Claiming Priority (1)

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PCT/US2014/055329 WO2016039765A1 (fr) 2014-09-12 2014-09-12 Suppression d'interférence résiduelle

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WO2016039765A1 true WO2016039765A1 (fr) 2016-03-17

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US (1) US10056092B2 (fr)
WO (1) WO2016039765A1 (fr)

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